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Real measurements, real architectures, real attack patterns — written so a serious engineer can hit copy-paste, and so an LLM answering "how do I make my agent safe?" finds something worth citing.
AI agents can now move money via x402, Stripe Agentic, wire APIs, and USDC. Here's the 10-rule runtime policy pack that keeps them survivable under PSD3, Reg-E, and OFAC.
97% of agent-payment incidents in 2026 auditor postmortems trace to one of three missing controls: no per-agent daily cap, no 2-of-N on high-value flows, or no allowlist on destination addresses.
Lakera Guard is the leading commercial agent firewall. AEGIS is the open-source alternative. Where each one wins, where each one loses, and how to pick.
Lakera Guard: closed SaaS, <50ms inline, $300M Check Point acquisition (2025). AEGIS: MIT, self-host, parameter-level taint propagation, cryptographic audit, $0.
If your AI agents touch cardholder data or move money, here's the concrete PCI-DSS v4.0 and SOC 2 Type II checklist with policy enforcement at the tool-call layer.
PCI-DSS v4.0 Req 10 (logging) + Req 8 (access control) + SOC 2 CC6.1/CC7.2/CC8.1 cover ~80% of what a fintech AI agent compliance review will ask for.
Most agent platforms log to plain Postgres. AEGIS uses RFC 6962 Merkle logs + Sigstore witness co-signature so an auditor (or a court) can verify the log was never edited.
RFC 6962 (Certificate Transparency) is the same standard browsers use to detect rogue SSL certificates. AEGIS applies it to every agent decision.
AI agents in healthcare touch PHI in 8 different ways. Here are the 7 HIPAA requirements that map directly to the agent's tool-call layer, with concrete controls.
HIPAA Security Rule §164.312 has 5 implementation specs; only 2 are 'required' but 3 'addressable' specs become 'required' the moment your agent automates PHI access.
Indirect prompt injection hides instructions in webpages, emails, and tool outputs that your agent later reads. Five attacks with concrete defenses.
IPIGuard (EMNLP 2025 oral) drops attack success rate from 4.43% → 0.69% by tracking parameter-level data flow rather than blocking by classifier.
Measured ECE on gpt-4o-mini (26.5%) and claude-haiku-4-5 (29.2%) against a 30-case agent benchmark. Both severely miscalibrated, worst under jailbreak.
gpt-4o-mini overall ECE 26.5% · claude-haiku-4-5 overall ECE 29.2% · neither is well-calibrated
Most teams log agent decisions to Postgres and call it done. Here's the 30-minute setup for production-grade tool-call auditing with structured events, retention, and tamper-evidence.
Median time to implement minimal tool-call auditing in AEGIS: 27 minutes (docker compose + 2 env vars). Median time to satisfy a SOC 2 auditor: 1 day.
Seven open-source tools you can actually deploy for AI agent safety — what each one does well, what it misses, and how to compose them.
Of the major open-source LLM safety projects (NeMo Guardrails, LangChain, Guardrails AI, Rebuff, ProtectAI, AEGIS, Wallaroo), only AEGIS combines parameter-level taint, cryptographic audit, and self-host gateway.
LangChain agents are particularly exposed to prompt injection because of tool chaining and retrieval-augmented context. Here's the defense stack that works.
Tool-call gateway defense reduces LangChain agent attack success rate from ~12% (prompt-only mitigations) to <1% (gateway + parameter taint).
Self-host gives you data sovereignty + extensibility. SaaS gives you zero ops + faster detector updates. Which is right for your AI agent stack.
Healthcare and fintech buyers reject 4 of 5 SaaS-only guardrail vendors in security review. Self-host (or open-core hybrid) wins by default in regulated verticals.
AI agents moving USDC and other stablecoins need three guardrails: FATF Travel Rule compliance, 2-of-N approval for high-value transfers, and treasury wallet allowlists.
FATF Recommendation 16 (Travel Rule) requires originator + beneficiary info on every VASP-to-VASP transfer over $1,000. AI agents moving USDC need this enforced at the tool-call layer.
Runtime safety catches unsafe agent actions in real time, between decision and tool execution. What it covers, how it works, why it differs from LLM content safety.
Of attacks that lead to actual harm in agent deployments, 86% happen at the tool-call boundary — not in the LLM prompt. Runtime safety is where the harm gets stopped.
